Integrating single-cell RNA sequencing with spatial transcriptomics reveals immune landscape for interstitial cystitis

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Abstract

Interstitial cystitis (IC) is a severely debilitating and chronic disorder with unclear etiology and pathophysiology, which makes the diagnosis difficult and treatment challenging. To investigate the role of immunity in IC bladders, we sequenced 135,091 CD45 ⁺ immune cells from 15 female patients with IC and 9 controls with stress urinary incontinence using single-cell RNA sequencing (scRNA-seq). 22 immune subpopulations were identified in the constructed landscape. Among them, M2-like macrophages, inflammatory CD14 ⁺ macrophages, and conventional dendritic cells had the most communications with other immune cells. Then, a significant increase of central memory CD4 ⁺ T cells, regulatory T cells, GZMK ⁺CD8 ⁺ T cells, activated B cells, un-switched memory B cells, and neutrophils, and a significant decrease of CD8 ⁺ effector T cells, Th17 cells, follicular helper T cells, switched memory B cells, transitional B cells, and macrophages were noted in IC bladders. The enrichment analysis identified a virus-related response during the dynamic change of cell proportion, furthermore, the human polyomavirus-2 was detected with a positive rate of 95% in urine of patients with IC. By integrating the results of scRNA-seq with spatial transcriptomics, we found nearly all immune subpopulations were enriched in the urothelial region or located close to fibroblasts in IC bladders, but they were discovered around urothelium and smooth muscle cells in control bladders. These findings depict the immune landscape for IC and might provide valuable insights into the pathophysiology of IC.

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Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Macrophages are heterogeneous and their phenotype and functions are regulated by the surrounding micro-environment. Macrophages commonly exist in two distinct subsets: 1) Classically activated or M1 macrophages, which are pro-inflammatory and polarized by lipopolysaccharide (LPS) either alone or in association with Th1 cytokines such as IFN-γ, GM-CSF, and produce pro-inflammatory cytokines such as interleukin-1β (IL-1β), IL-6, IL-12, IL-23, and TNF-α; and 2) Alternatively activated or M2 macrophages, which are anti-inflammatory and immunoregulatory and polarized by Th2 cytokines such as IL-4 and IL-13 and produce anti-inflammatory cytokines such as IL-10 and TGF-β. M1 and M2 macrophages have different functions and transcriptional profiles. They have unique abilities by destroying pathogens or repair the inflammation-associated injury. It is known that M1/M2 macrophage balance polarization governs the fate of an organ in inflammation or injury. When the infection or inflammation is severe enough to affect an organ, macrophages first exhibit the M1 phenotype to release TNF-α, IL-1β, IL-12, and IL-23 against the stimulus. But, if M1 phase continues, it can cause tissue damage. Therefore, M2 macrophages secrete high amounts of IL-10 and TGF-β to suppress the inflammation, contribute to tissue repair, remodeling, vasculogenesis, and retain homeostasis. In this review, we first discuss the basic biology of macrophages including origin, differentiation and activation, tissue distribution, plasticity and polarization, migration, antigen presentation capacity, cytokine and chemokine production, metabolism, and involvement of microRNAs in macrophage polarization and function. Secondly, we discuss the protective and pathogenic role of the macrophage subsets in normal and pathological pregnancy, anti-microbial defense, anti-tumor immunity, metabolic disease and obesity, asthma and allergy, atherosclerosis, fibrosis, wound healing, and autoimmunity.

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Author and article information

Contributors

Hong Li: lihonghxhx@scu.edu.cn

De-yi Luo: luodeyi1985@163.com

Journal

Journal ID (nlm-ta): Signal Transduct Target Ther

Journal ID (iso-abbrev): Signal Transduct Target Ther

Title: Signal Transduction and Targeted Therapy

Publisher: Nature Publishing Group UK (London )

ISSN (Print): 2095-9907

ISSN (Electronic): 2059-3635

Publication date (Electronic): 20 May 2022

Publication date PMC-release: 20 May 2022

Publication date Collection: 2022

Volume: 7

Electronic Location Identifier: 161

Affiliations

GRID grid.13291.38, ISNI 0000 0001 0807 1581, Department of Urology, Institute of Urology, West China Hospital, , Sichuan University, ; Chengdu, Sichuan P.R. China

Article

Publisher ID: 962

DOI: 10.1038/s41392-022-00962-8

PMC ID: 9120182

PubMed ID: 35589692

SO-VID: 3a120d17-8644-41d7-955f-71ed88f25e2d

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 22 November 2021

Date revision received : 10 March 2022

Date accepted : 14 March 2022

Funding

Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);

Award ID: 81770673

Award ID: 32171301

Award Recipient : De-yi Luo

Funded by: 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant Nos. ZY2017310); National Key Research and Development Program of China (No. 2021YFC2009100 and 2021YFC2009102).